Proteomics is the field of bioscience involving the characterization of the proteins encoded by the human genome, and enabled by the gene sequence data produced by the human genome project. Since the structure of a protein is key to understanding its function, one field of proteomics in particular has rapidly emerged concerning high throughput structure determination or structural genomics. In determining protein structure, the proteins are first crystallized, and then an X-ray generator produces diffraction patterns from which a three-dimensional picture of the atomic arrangement in the crystal can be obtained. Advances in macromolecular crystallography techniques, computer speed, and the availability of high-energy synchrotron x-ray sources, make rapid structure determination possible given adequate quality protein crystals.
Crystal growth, however, is difficult because proteins are large, irregularly shaped molecules that do not readily come together in a repeating pattern, and the complete set of crystallization conditions is too large and impractical to screen comprehensively. Thus, previously uncrystallized proteins must be screened on a trial and error basis against a large array of conditions that have the potential to induce crystal formation. Automated methods using, for example, robotic liquid handling devices, robotic CCD-based microscope cameras, or light microscopes equipped with robotic stages and CCD cameras, have been developed and are commercially available to speed up the process of setting up and recording the results (automated image capture) of a large number of crystallization trials. However, a practical problem remains in that each experiment must still be visually inspected to determine successful crystal formation. In fact, the high throughput enabled by the automation in setup and image-capture has increased the visual inspection bottleneck, which is typically performed manually by human intervention.
One example of an automated crystal detection method developed to address the visual inspection bottleneck is disclosed in the article “Intelligent Decision Support for Protein Crystal Growth” (by Jurisica et al, IBM Systems Journal, Vol. 40, No. 2, 2001). In that article, and as shown in FIGS. 3–7 thereof, images of screening results are analyzed using a two-dimensional Fourier transform. In particular, FIG. 5C illustrates the Fourier frequency spectrum used in the analysis, and FIG. 5D illustrates an analysis of the spectrum derivatives and circular averages to provide features information of the image. From this feature extraction and analysis, the outcome of the experiment is classified as, clear drop, amorphous precipitate, phase separation, microcrystals, crystals, or unknown.
Despite such efforts, difficulties in automating (i.e. without human intervention) crystal detection remain due to such factors as poor image quality due to noise and low contrast, differences in crystal shapes, poorly formed crystals, etc. With respect to poor image quality, crystals may have less contrast relative to the background than other objects or particles. For example, the difference between the crystal and the background based on 256 gray levels is often 15 levels, whereas the difference for dirt is usually above 40 levels. Additionally, many different crystal shapes exist due to, for example, the existence of several large classes of crystal shape, the picture is a 2-D projection of a 3-D object, crystal imperfections with faulty edges, and large variations in crystal size, e.g. ranging from about 10 μm to nearly 300 μm. There are also many things on the picture that are not real crystals, such as dirt, precipitation, quasi-crystals, small drop due to condensation, and unidentified effects. Additionally, an automated crystal detection process must also achieve a high threshold of accuracy by being able to identify virtually all crystals with a low false-positive rate.
Thus in summary, there is a need for an automated crystal detection method and system for inspecting two-dimensional images and successfully detecting crystals therefrom. An automated solution for crystal detection, such as implemented by a software program, would be a great labor savor by possessing the capability of processing thousands of images a day and provide analysis substantially free from false positives.